The role of ocean transport in the uptake of anthropogenic CO2

We compare modeled oceanic carbon uptake in response to pulse CO2 emissions using a suite of global ocean models and Earth system models. In response to a CO2 pulse emission of 590 Pg C (corresponding to an instantaneous doubling of atmospheric CO2 from 278 to 556 ppm), the fraction of CO2 emitted that is absorbed by the ocean is: 37±8%, 56±10%, and 81±4% (model mean ±2σ ) in year 30, 100, and 1000 after the emission pulse, respectively. Modeled oceanic uptake of pulse CO2 on timescales from decades to about a century is strongly correlated with simulated present-day uptake of chlorofluorocarbons (CFCs) and CO2 across all models, while the amount of pulse CO2 absorbed by the ocean from a century to a millennium is strongly correlated with modeled radiocarbon in the deep Southern and Pacific Ocean. However, restricting the analysis to models that are capable of reproducing observations within uncertainty, the correlation is generally much weaker. The rates of surface-to-deep ocean transport are determined for individual models from the instantaneous doubling CO2 simulations, and they are used to calculate oceanic CO2 uptake in response to pulse CO2 emissions of different sizes pulses of 1000 and 5000 Pg C. These results are compared with simulated oceanic uptake of CO2 by a number of models simulations with the coupling of climate-ocean carbon cycle and without it. This comparison demonstrates that the impact of different ocean transport rates across models on oceanic uptake of anthropogenic CO2 is of similar magnitude as that of climate-carbon cycle feedbacks in a single model, emphasizing the important role of ocean transport in the uptake of anthropogenic CO2.

Incorporating Latent Variables into Discrete Choice Models - A Simultaneous Estimation Approach Using SEM Software

Integrated choice and latent variable (ICLV) models represent a promising new class of models which merge classic choice models with the structural equation approach (SEM) for latent variables. Despite their conceptual appeal, applications of ICLV models in marketing remain rare. We extend previous ICLV applications by first estimating a multinomial choice model and, second, by estimating hierarchical relations between latent variables. An empirical study on travel mode choice clearly demonstrates the value of ICLV models to enhance the understanding of choice processes. In addition to the usually studied directly observable variables such as travel time, we show how abstract motivations such as power and hedonism as well as attitudes such as a desire for flexibility impact on travel mode choice. Furthermore, we show that it is possible to estimate such a complex ICLV model with the widely available structural equation modeling package Mplus. This finding is likely to encourage more widespread application of this appealing model class in the marketing field.

Modeling the stratospheric warming following the Mt. Pinatubo eruption: uncertainties in aerosol extinctions

In terms of atmospheric impact, the volcanic eruption of Mt. Pinatubo (1991) is the best characterized large eruption on record. We investigate here the model-derived stratospheric warming following the Pinatubo eruption as derived from SAGE II extinction data including recent improvements in the processing algorithm. This method, termed SAGE_4λ, makes use of the four wavelengths (385, 452, 525 and 1024 nm) of the SAGE II data when available, and uses a data-filling procedure in the opacity-induced "gap" regions. Using SAGE_4λ, we derived aerosol size distributions that properly reproduce extinction coefficients also at much longer wavelengths. This provides a good basis for calculating the absorption of terrestrial infrared radiation and the resulting stratospheric heating. However, we also show that the use of this data set in a global chemistry–climate model (CCM) still leads to stronger aerosol-induced stratospheric heating than observed, with temperatures partly even higher than the already too high values found by many models in recent general circulation model (GCM) and CCM intercomparisons. This suggests that the overestimation of the stratospheric warming after the Pinatubo eruption may not be ascribed to an insufficient observational database but instead to using outdated data sets, to deficiencies in the implementation of the forcing data, or to radiative or dynamical model artifacts. Conversely, the SAGE_4λ approach reduces the infrared absorption in the tropical tropopause region, resulting in a significantly better agreement with the post-volcanic temperature record at these altitudes.

Estimating and quantifying uncertainties on level sets using the Vorob'ev expectation and deviation with Gaussian process models

Several methods based on Kriging have recently been proposed for calculating a probability of failure involving costly-to-evaluate functions. A closely related problem is to estimate the set of inputs leading to a response exceeding a given threshold. Now, estimating such a level set—and not solely its volume—and quantifying uncertainties on it are not straightforward. Here we use notions from random set theory to obtain an estimate of the level set, together with a quantification of estimation uncertainty. We give explicit formulae in the Gaussian process set-up and provide a consistency result. We then illustrate how space-filling versus adaptive design strategies may sequentially reduce level set estimation uncertainty.

Stochastic search for semiparametric linear regression models

This paper introduces and analyzes a stochastic search method for parameter estimation in linear regression models in the spirit of Beran and Millar [Ann. Statist. 15(3) (1987) 1131–1154]. The idea is to generate a random finite subset of a parameter space which will automatically contain points which are very close to an unknown true parameter. The motivation for this procedure comes from recent work of Dümbgen et al. [Ann. Statist. 39(2) (2011) 702–730] on regression models with log-concave error distributions.

A nonlinear QCT-based finite element model validation study for the human femur tested in two configurations in vitro

Purpose Femoral fracture is a common medical problem in osteoporotic individuals. Bone mineral density (BMD) is the gold standard measure to evaluate fracture risk in vivo. Quantitative computed tomography (QCT)-based homogenized voxel finite element (hvFE) models have been proved to be more accurate predictors of femoral strength than BMD by adding geometrical and material properties. The aim of this study was to evaluate the ability of hvFE models in predicting femoral stiffness, strength and failure location for a large number of pairs of human femora tested in two different loading scenarios. Methods Thirty-six pairs of femora were scanned with QCT and total proximal BMD and BMC were evaluated. For each pair, one femur was positioned in one-legged stance configuration (STANCE) and the other in a sideways configuration (SIDE). Nonlinear hvFE models were generated from QCT images by reproducing the same loading configurations imposed in the experiments. For experiments and models, the structural properties (stiffness and ultimate load), the failure location and the motion of the femoral head were computed and compared. Results In both configurations, hvFE models predicted both stiffness (R2=0.82 for STANCE and R2=0.74 for SIDE) and femoral ultimate load (R2=0.80 for STANCE and R2=0.85 for SIDE) better than BMD and BMC. Moreover, the models predicted qualitatively well the failure location (66% of cases) and the motion of the femoral head. Conclusions The subject specific QCT-based nonlinear hvFE model cannot only predict femoral apparent mechanical properties better than densitometric measures, but can additionally provide useful qualitative information about failure location.

Nuclear organization in the nematode C. elegans.

With its invariant cell lineage, easy genetics and small genome, the nematode Caenorhabditis elegans has emerged as one of the prime models in developmental biology over the last 50 years. Surprisingly however, until a decade ago very little was known about nuclear organization in worms, even though it is an ideal model system to explore the link between nuclear organization and cell fate determination. Here, we review the latest findings that exploit the repertoire of genetic tools developed in worms, leading to the identification of important sequences and signals governing the changes in chromatin tridimensional architecture. We also highlight parallels and differences to other model systems.

Effects of exogenous surfactant on the non-heart-beating donor lung graft in experimental lung transplantation - a stereological study.

The use of non-heart-beating donor (NHBD) lungs may help to overcome the shortage of lung grafts in clinical lung transplantation, but warm ischaemia and ischaemia/reperfusion injury (I/R injury) resulting in primary graft dysfunction represent a considerable threat. Thus, better strategies for optimized preservation of lung grafts are urgently needed. Surfactant dysfunction has been shown to contribute to I/R injury, and surfactant replacement therapy is effective in enhancing lung function and structural integrity in related rat models. In the present study we hypothesize that surfactant replacement therapy reduces oedema formation in a pig model of NHBD lung transplantation. Oedema formation was quantified with (SF) and without (non-SF) surfactant replacement therapy in interstitial and alveolar compartments by means of design-based stereology in NHBD lungs 7 h after cardiac arrest, reperfusion and transplantation. A sham-operated group served as control. In both NHBD groups, nearly all animals died within the first hours after transplantation due to right heart failure. Both SF and non-SF developed an interstitial oedema of similar degree, as shown by an increase in septal wall volume and arithmetic mean thickness as well as an increase in the volume of peribron-chovascular connective tissue. Regarding intra-alveolar oedema, no statistically significant difference could be found between SF and non-SF. In conclusion, surfactant replacement therapy cannot prevent poor outcome after prolonged warm ischaemia of 7 h in this model. While the beneficial effects of surfactant replacement therapy have been observed in several experimental and clinical studies related to heart-beating donor lungs and cold ischaemia, it is unlikely that surfactant replacement therapy will overcome the shortage of organs in the context of prolonged warm ischaemia, for example, 7 h. Moreover, our data demonstrate that right heart function and dysfunctions of the pulmonary vascular bed are limiting factors that need to be addressed in NHBD.

In vitro fabrication of autologous living tissue-engineered vascular grafts based on prenatally harvested ovine amniotic fluid-derived stem cells

Amniotic fluid cells (AFCs) have been proposed as a valuable source for tissue engineering and regenerative medicine. However, before clinical implementation, rigorous evaluation of this cell source in clinically relevant animal models accepted by regulatory authorities is indispensable. Today, the ovine model represents one of the most accepted preclinical animal models, in particular for cardiovascular applications. Here, we investigate the isolation and use of autologous ovine AFCs as cell source for cardiovascular tissue engineering applications. Fetal fluids were aspirated in vivo from pregnant ewes (n = 9) and from explanted uteri post mortem at different gestational ages (n = 91). Amniotic non-allantoic fluid nature was evaluated biochemically and in vivo samples were compared with post mortem reference samples. Isolated cells revealed an immunohistochemical phenotype similar to ovine bone marrow-derived mesenchymal stem cells (MSCs) and showed expression of stem cell factors described for embryonic stem cells, such as NANOG and STAT-3. Isolated ovine amniotic fluid-derived MSCs were screened for numeric chromosomal aberrations and successfully differentiated into several mesodermal phenotypes. Myofibroblastic ovine AFC lineages were then successfully used for the in vitro fabrication of small- and large-diameter tissue-engineered vascular grafts (n = 10) and cardiovascular patches (n = 34), laying the foundation for the use of this relevant pre-clinical in vivo assessment model for future amniotic fluid cell-based therapeutic applications. Copyright © 2013 John Wiley & Sons, Ltd.

Role and Regulation of EphA2 in Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cancer cause of death in the US. Gemcitabine is the first-line therapy for this disease, but unfortunately it shows only very modest benefit. The focus of the current study was to investigate the role and regulation of EphA2, a receptor tyrosine kinase expressed in PDAC, to further understand this disease and identify new therapeutic targets. The role of EphA2 was determined in PDAC by siRNA mediated silencing. In combination with gemcitabine, silencing of EphA2 caused a dramatic increase in apoptosis even in highly resistant cells in vitro. Furthermore, EphA2 silencing was found to be useful in 2 orthotopic models in vivo: 1) shRNA-pretreated Miapaca-2 cells, and 2) in vivo delivery of siRNA to established MPanc96 tumors. Silencing of EphA2 alone reduced tumor growth in Miapaca-2 cells. In MPanc96, only the combination treatment of gemcitabine plus siEphA2 significantly reduced tumor growth, as well as the number of lung and liver metastases. Taken together, these observations support EphA2 as a target for combination therapies for PDAC. The regulation of EphA2 was further explored with a focus on the role of Ras. K-Ras activating mutations are the most important initiating event in PDAC. We demonstrated that Ras regulates EphA2 expression through activation of MEK2 and phosphorylation of ERK. Downstream of ERK, silencing of the transcription factor AP-1 subunit c-Jun or inhibition of the ERK effector RSK caused a decrease in EphA2 expression, supporting their roles in this process. Further examination of Ras/MEK/ERK pathway modulators revealed that PEA-15, a protein that sequesters ERK to the cytoplasm, inhibited expression of EphA2. A significant inverse correlation between EphA2 and PEA-15 levels was observed in mouse models of PDAC. In cells where an EGFR inhibitor reduced phospho-Erk, expression of EphA2 was also reduced, indicating that changes in EphA2 levels may allow monitoring the effectiveness of anti-Ras/MEK/ERK therapies. In conclusion, EphA2 levels may be a good prognostic factor for anti-EGFR/anti-Ras therapies, and EphA2 itself is a relevant target for the development of new therapies.

Antibodies targeting hepatoma-derived growth factor as a novel strategy in treating lung cancer.

Hepatoma-derived growth factor (HDGF) is overexpressed in lung cancer and the overexpression correlates with aggressive biological behaviors and poor clinical outcomes. We developed anti-HDGF monoclonal antibodies and tested their antitumor activity in lung cancer xenograft models. We also determined biological effects in tumors treated with the antibody alone or in combination with bevacizumab/avastin (an anti-vascular endothelial growth factor antibody) and/or gemcitabine (a chemotherapeutic agent). We found the anti-HDGF was effective to inhibit tumor growth in non-small cell lung cancer xenograft models. In the A549 model, compared with control IgG, tumor growth was substantially inhibited in animals treated with anti-HDGF antibodies, particularly HDGF-C1 (P = 0.002) and HDGF-H3 (P = 0.005). When HDGF-H3 was combined with either bevacizumab or gemcitabine, we observed enhanced tumor growth inhibition, particularly when the three agents were used together. HDGF-H3-treated tumors exhibited significant reduction of microvessel density with a pattern distinctive from the microvessel reduction pattern observed in bevacizumab-treated tumors. HDGF-H3-treated but not bevacizumab-treated tumors also showed a significant increase of apoptosis. Interestingly, many of the apoptotic cells in HDGF-H3-treated tumors are stroma cells, suggesting that the mechanism of the antitumor activity is, at least in part, through disrupting formation of tumor-stroma structures. Our results show that HDGF is a novel therapeutic target for lung cancer and can be effectively targeted by an antibody-based approach.

Predictive Margins and Marginal Effects in Stata

Tables of estimated regression coefficients, usually accompanied by additional information such as standard errors, t-statistics, p-values, confidence intervals or significance stars, have long been the preferred way of communicating results from statistical models. In recent years, however, the limits of this form of exposition have been increasingly recognized. For example, interpretation of regression tables can be very challenging in the presence of complications such as interaction effects, categorical variables, or nonlinear functional forms. Furthermore, while these issues might still be manageable in the case of linear regression, interpretational difficulties can be overwhelming in nonlinear models such as logistic regression. To facilitate sensible interpretation of such models it is often necessary to compute additional results such as marginal effects, predictive margins, or contrasts. Moreover, smart graphical displays of results can be very valuable in making complex relations accessible. A number of helpful commands geared at supporting these tasks have been recently introduced in Stata, making elaborate interpretation and communication of regression results possible without much extra effort. Examples of such commands are -margins-, -contrasts-, and -marginsplot-. In my talk, I will discuss the capabilities of these commands and present a range of examples illustrating their use.

Sequential updating via dynamic hierarchical models: A Bayesian approach to longitudinal data analysis

Most statistical analysis, theory and practice, is concerned with static models; models with a proposed set of parameters whose values are fixed across observational units. Static models implicitly assume that the quantified relationships remain the same across the design space of the data. While this is reasonable under many circumstances this can be a dangerous assumption when dealing with sequentially ordered data. The mere passage of time always brings fresh considerations and the interrelationships among parameters, or subsets of parameters, may need to be continually revised. ^ When data are gathered sequentially dynamic interim monitoring may be useful as new subject-specific parameters are introduced with each new observational unit. Sequential imputation via dynamic hierarchical models is an efficient strategy for handling missing data and analyzing longitudinal studies. Dynamic conditional independence models offers a flexible framework that exploits the Bayesian updating scheme for capturing the evolution of both the population and individual effects over time. While static models often describe aggregate information well they often do not reflect conflicts in the information at the individual level. Dynamic models prove advantageous over static models in capturing both individual and aggregate trends. Computations for such models can be carried out via the Gibbs sampler. An application using a small sample repeated measures normally distributed growth curve data is presented. ^

Culturally sensitive cancer education: Effect on knowledge, cancer fatalism, and participation in mobile mammography screening among African-American women

Purpose. To evaluate the effectiveness of a culturally sensitive educational intervention that used an African American lay survivor of breast cancer to increase knowledge of breast cancer, decrease cancer fatalism, and increase participation in mobile mammography screening among African American women. ^ Design. Experimental pretest/posttest design. ^ Setting. Two predominantly African American churches in a large southwestern metropolitan city. ^ Sample. Participants included 93 African American women, 40 years of age and older. Participants were randomly assigned to an intervention group (n = 48) or a control group (n = 45). ^ Methods. Pretest and post-test measures included the Breast Cancer Knowledge Test and the Powe Fatalism Inventory. In addition, demographic and breast screening practices were collected by questionnaire. The intervention group received a breast cancer educational testimonial from an African American lay survivor of breast cancer, who answered questions and addressed concerns, while stressing the importance of taking responsibility for one's own health and spreading disease prevention messages throughout the African American community. The control group viewed the American Cancer Society “Keep In Touch” video prepared specifically for African American women. Participants in both groups were given culturally sensitive educational materials designed to increase knowledge about breast cancer, and were instructed on breast self-examination by an African American registered nurse, using ethnically appropriate breast models. In addition, after the post-test, all eligible participants were given an opportunity to have a free mammogram via a mobile mammography unit parked at the church. ^ Findings. Participants in the intervention group had a significant increase (p = .03) in knowledge of breast cancer and a significant decrease (p = .000) in fatalism scores compared to those individuals in the control group. The intervention group had a 61% participation rate in screening, while the control group had a 39% participation rate in screening. However, the difference was not statistically significant at the .05 level (p = .07). ^ Conclusions. Results demonstrate that culturally sensitive breast cancer education is successful in increasing knowledge and decreasing cancer fatalism. While there was a trend toward behavior change in the intervention group, more research needs to be done in this area. ^

Motivational patterns as an instrument for predicting success in promising young football players

Psychological characteristics are crucial to identifying talents, which is why these are being incorporated in today’s multidimensional talent models. In addition to multidimensionality, talent studies are increasingly drawing on holistic theories of development, leading to the use of person-oriented approaches. The present study adopts such an approach by looking at the influence that motivational characteristics have on the development of performance, in a person-oriented way. For this purpose, it looks at how the constructs achievement motive, achievement goal orientation and self-determination interact with one another, what patterns they form and how these patterns are linked to subsequent sports success. 97 top young football players were questioned twice. Another year later, it was enquired which of these players had been selected for the U15 national team. At both measuring points, four patterns were identified, which displayed a high degree of structural and individual stability. As expected, the highly intrinsically achievement-oriented players were significantly more likely to move up into the U15 national team. The results point to the importance of favourable patterns of motivational variables in the form of specific types, for medium-term performance development among promising football talents, and thus provide valuable clues for the selection and promotion of those.